An abundance of data is scattered throughout today's accounting sys tems. Technological advances and sophisticated analysis techniques allow these bits and bytes to be the building blocks for significant improvements in decision making.
What is needed is a systems architecture to identify strategic information in existing systems and make it available for analysis and inquiries. Through such a program, many companies have found data patterns of strategic significance. For example, a music chain learned that peo ple older than 65 bought many rap and alternative music CDs. These buyers had not changed their tastes for music; they were buying Christmas presents for their grandchildren. A target marketing program to this group increased sales by 37%.
Exhibit 1 illustrates the overall operation of the system. There are two primary steps: data acquisition and cleansing, and data analysis.
Data Acquisition and Cleansing A typical company may find the following conditions: The marketing system contains data about promotions sent to customers. The order entry system contains details about customers' orders. The accounts receivable system contains customers' payment histories. Data about customers is spread across several legacy systems.
Few people compile a complete view of the information, from promotions to final collections, because data must be pulled from multiple sources. The process becomes more complicated if each system stores data in unique ways. For example, marketing refers to the customer by name, order entry is organized by purchase order number, and accounts receivable is keyed to the customer's account number.
To overcome these difficulties, many companies have copied the data decision makers need from the legacy systems to a data repository. Special software automates this repetitious task. During the transfer, the data is "cleansed." Cleansing reconciles the dissimilarities, handles missing values, and integrates the data into the repository in a consistent fashion. In the previous example, one key would be used to identify each customer in the data repository.
In addition to internal data, decision makers often require external data such as average income levels, other demographics, and economic forecasts. One systems manager reported that users ran two-thirds of their queries against information from external sources. To improve access, many companies have found the data repository a convenient place to store such information.
Several different names are commonly used for data repositories, depending upon the type of data stored and their intended use. For example, a data warehouse is a repository that draws data from many systems that will be shared by many users. A data mart contains data drawn from fewer systems that will be used by one or a few functional units. A multidimensional database provides different views of financial data: by product, channel, or time of sale, to name a few.
One advantage of moving data to a single repository is that decision makers can obtain views and analyses of data not previously possible. For example, marketing personnel may be able to better evaluate the effectiveness of promotions when they can easily match information about the promotions sent to customers with the orders received.
A second advantage is time savings. One organization has 200 users running 5,000 queries per month against its data repository. By using the data repository, each person saves one hour per query over the previous data collection efforts. This amounts to a substantial savings of 60,000 hours per year.
A third advantage is that the existing legacy transaction systems will remain intact. Thus, the many people that process transactions on a day-today basis do not need any retraining. Furthermore, there is no disruption of business processes caused by the conversion to other transaction processing systems. …